% !TEX root = ArticoloRF.tex
%Project Details Introduction
To evaluate different algorithms for the construction of decision trees, a modularized version of a Random Forest Classifier has been implemented. 
The programming language of choice has been C++ because the resulting software is usually faster in terms of performances compared to other languages.
The software has been parameterized giving the users the opportunity to specify the number of trees in the forest, the depth of the trees and the type of algorithm to be employed among the following:
\begin{itemize}
\item Extremely Randomized Trees;
\item Gini Index for feature and threshold selection;
\item Information Gain for feature selection;
\item Fisher’s Linear Discriminant Analysis for data projection and threshold selection;
\end{itemize}
The implemented algorithms will be further detailed in the following sections.